问题描述
我有一个 (540, 960, 1) 形状的图像，其值范围为黑白的 [0..255].我需要将其转换为热图"表示.例如，具有 255 的像素应该是最热的，而具有 0 的像素应该是最不热的.其他介于两者之间.我还需要将热图作为 Numpy 数组返回，以便稍后将它们合并到视频中.有没有办法做到这一点?
这里有两种方法，一种使用Matplotlib，一种只使用OpenCV
方法#1: OpenCV +
我们可以使用 OpenCV 内置的热图功能.这是使用 cv2.COLORMAP_HOT 热图
代码
导入 cv2图像 = cv2.imread('frame.png', 0)热图 = cv2.applyColorMap(图像，cv2.COLORMAP_HOT)cv2.imshow('热图', 热图)cv2.waitKey()注意:虽然 OpenCV 的内置实现简短快捷，但我建议使用方法 #1，因为有更大的颜色图选择.Matplotlib 有 数百种不同的颜色图并允许您在 OpenCV 时创建自己的自定义颜色图只有12个可供选择.这是内置的 OpenCV 颜色图选择:
I have a (540, 960, 1) shaped image with values ranging from [0..255] which is black and white. I need to convert it to a "heatmap" representation. As an example, pixels with 255 should be of most heat and pixels with 0 should be with least heat. Others in-between. I also need to return the heat maps as Numpy arrays so I can later merge them to a video. Is there a way to achieve this?
Here are two methods, one using Matplotlib and one using only OpenCV
Method #1: OpenCV + matplotlib.pyplot.get_cmap
To implement a grayscale (1-channel) -> heatmap (3-channel) conversion, we first load in the image as grayscale. By default, OpenCV reads in an image as 3-channel, 8-bit BGR.
We can directly load in an image as grayscale using cv2.imread() with the cv2.IMREAD_GRAYSCALE parameter or use cv2.cvtColor() to convert a BGR image to grayscale with the cv2.COLOR_BGR2GRAY parameter. Once we load in the image, we throw this grayscale image into Matplotlib to obtain our heatmap image. Matplotlib returns a RGB format so we must convert back to Numpy format and switch to BGR colorspace for use with OpenCV. Here's a example using a scientific infrared camera image as input with the inferno colormap. See choosing color maps in Matplotlib for available built-in colormaps depending on your desired use case.
Input image:
Output heatmap image:
Code
import matplotlib.pyplot as plt
import numpy as np
import cv2
image = cv2.imread('frame.png', 0)
colormap = plt.get_cmap('inferno')
heatmap = (colormap(image) * 2**16).astype(np.uint16)[:,:,:3]
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR)
cv2.imshow('image', image)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()
Method #2: cv2.applyColorMap()
We can use OpenCV's built in heatmap function. Here's the result using the cv2.COLORMAP_HOT heatmap
Code
import cv2
image = cv2.imread('frame.png', 0)
heatmap = cv2.applyColorMap(image, cv2.COLORMAP_HOT)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()
Note: Although OpenCV's built-in implementation is short and quick, I recommend using Method #1 since there is a larger colormap selection. Matplotlib has hundreds of various colormaps and allows you to create your own custom color maps while OpenCV only has 12 to choose from. Here's the built in OpenCV colormap selection:
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